Image processing apparatus and image processing method

ABSTRACT

An image processing apparatus includes a display and processing circuitry. The processing circuitry generates second image data by subtracting first image data before and after contrast enhancement. The processing circuitry removes information indicating a structure from pieces of the second image data to generate third image data. The processing circuitry correct misalignment of the third image data based on characteristics of the shape of the subject represented in at least any one of the first to third image data. The processing circuitry obtains a variation index between the third image data after alignment correction, and generates fourth image data base on the variation index. The processing circuitry displays a medical image based on the fourth image data on the display.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority fromJapanese Patent Application Nos. 2012-261880, filed 30 Nov. 2012 and2013-247618, filed 29 Nov. 2013; the entire contents of which areincorporated herein by reference.

FIELD

Embodiments described herein relate generally to an image processingapparatus and an image processing method.

BACKGROUND

There is a method of calculating the amount of blood flow such ascerebral blood volume (CBV) by using a modality such as an X-raycomputed tomography (CT) system or an angio system (an X-ray imagingapparatus). For example, the CBV is calculated based on the profile ofCT values obtained by continuous scanning. For another example, the CBVis calculated by using a non-contrast image acquired before theinjection of a contrast agent and a contrast image acquired after theinjection. Specifically, an image of blood vessels including capillariesis created by removing non-vascular regions such as bones and softtissues from the difference between the non-contrast image and thecontrast image. The generated image is displayed, for example, as acolor map.

This method is used to determine the results of procedure, therapy, ortreatment for a subject. However, it may sometimes be required to make adifferent decision according to the state of the subject before aprocedure. Besides, the blood flow velocity or the like may varyaccording to the psychological and physical condition of the subject whois to undergo a procedure or the like. In this case, even if the amountof blood flow is calculated before or after a predetermined event suchas a procedure, it may be difficult to determine whether the calculationresult is influenced by the predetermined event such as a procedure. Forexample, even if the CBV is calculated based on an examination resultobtained after the predetermined procedure, a viewer of an image may notdetermine whether a portion, which appears in the image or the like as aresult of measurement, emerges after the procedure. In such a case, forexample, an affected part or the like that has already existed beforethe procedure may be mistaken for a newly developed affected part.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an image processing apparatus according toan embodiment;

FIG. 2 is a diagram for explaining the outline of the generation andanalysis of image data in the embodiment; and

FIG. 3 is a flowchart of a series of the operation of the imageprocessing apparatus of the embodiment.

DETAILED DESCRIPTION

In general, according to one embodiment, an image processing apparatusincludes a subtraction data generator, an analyzer, a registrationprocessor, an image processor, a display, and a display controller. Thesubtraction data generator generates second image data by subtractingfirst image data after contrast enhancement from first image data beforecontrast enhancement. The analyzer removes information corresponds toanatomical structures from a plurality of the second image data togenerate third image data. The registration processor corrects thepositions of a plurality of the third image data obtained at differenttimings based on structural information represented on image in at leastany one of the first image data, the second image data, and the thirdimage data. The image processor obtains a variation index between thethird image data after correcting relative positions, and generatesfourth image data base on the variation index. The display controllerdisplays a medical image based on the fourth image data on the display.

With reference to FIG. 1, a description is given for a configuration ofan image processing apparatus according to an embodiment. As illustratedin FIG. 1, an image processing apparatus 100 of this embodiment includesa projection data storage 10, a reconstruction processor 11, an analyzer12, an image data storage 20, a registration processor 21, an imageprocessor 22, a display controller 23, and a display 24. As illustratedin FIG. 1, the image processing apparatus 100 of this embodimentreceives projection data acquired before and after contrast enhancementby an imaging unit 500. The image processing apparatus 100 generatesimage data based on the projection data. The imaging unit 500 is apredetermined modality such as an X-ray CT system or an angio system (anX-ray imaging apparatus). The image data is, for example, volume data.

The projection data storage 10 receives projection data acquired atdifferent times by the imaging unit 500. The projection data includes,for example, those obtained prior to a predetermined event (procedure,therapy, or treatment for a subject), and those obtained after thepredetermined event. Besides, the projection data can be divided intoprojection data D11 a, D12 a, . . . before contrast enhancement andprojection data D11 b, D12 b, . . . after contrast enhancement. Theprojection data storage 10 stores the projection data in associationwith examination or treatment information, pre- or post-contrastenhancement, and acquisition time. In the following, let us assume thatprojection data acquired prior to a predetermined event and projectiondata acquired after the predetermined event are acquired at differentexaminations. In addition, the projection data D11 a and the projectiondata D11 b are projection data before and after contrast enhancementacquired in the same examination prior to a predetermined event.Similarly, the projection data D12 a and the projection data D12 b areprojection data before and after contrast enhancement acquired in thesame examination after the predetermined event. Further, thepredetermined event is described as “procedure”. Accordingly,hereinafter “procedure” does not always refer to procedure, but may alsorefer to therapy or treatment for a subject.

The reconstruction processor 11 of the embodiment includes a subtractiondata generator 111. In the following, the operation of thereconstruction processor 11 is described first, and then the operationof the subtraction data generator 111 afterward.

The reconstruction processor 11 retrieves projection data, thatexamination and treatment information, pre- or post-contrastenhancement, and the acquisition time from the projection data storage10. When the projection data D11 a before contrast enhancement and theprojection data D11 b after contrast enhancement acquired prior to apredetermined event are retrieved, the reconstruction processor 11performs reconstruction processing after subtracting the projection dataD11 b from the projection data D11 a. Specifically, the reconstructionprocessor 11 generates image data (tomographic image data or volumedata) by a reconstruction algorithm such as Feldkamp algorithm, mostfamous 3D backprojection algorithm. The reconstruction processor 11 canreconstruct volume data by any method such as three-dimensional Fouriertransform, convolution back projection or the like, or cone-beamreconstruction, multi-slice reconstruction, iterative reconstruction, orthe like. Hereinafter, image data reconstructed from projection databefore contrast enhancement (e.g., the projection data D11 a, D12 a, . .. ) is sometimes called as “image data before contrast enhancement”.Similarly, image data reconstructed from projection data after contrastenhancement (e.g., the projection data D11 b, D12 b, . . . ) issometimes called as “image data after contrast enhancement”. The imagedata before and after contrast enhancement correspond to an example of“first image data”.

In this manner, the reconstruction processor 11 performs reconstructionprocessing by using the projection data D11 a and the projection dataD11 b, acquired prior to a procedure, based on predeterminedreconstruction conditions. Thereby, the reconstruction processor 11generates image data D21 a before contrast enhancement and image dataD21 b after contrast enhancement. On the other hand, when the projectiondata D12 a and the projection data D12 b, acquired after the procedure,are retrieved, the reconstruction processor 11 performs reconstructionprocessing by using them, and generates image data D22 a before contrastenhancement and image data D22 b after contrast enhancement.

The reconstruction processor 11 may perform the reconstructionprocessing described above according to an instruction from an operator.In this case, the reconstruction processor 11 receives the determinationof examination from the operator. The reconstruction processor 11 thenretrieves projection data corresponding to the examination andreconstructs it.

The reconstruction processor 11 stores, in the image data storage 20,the image data D21 a before contrast enhancement and the image data D21b after contrast enhancement generated prior to a procedure inassociation with each other. Besides, the reconstruction processor 11sends the image data D21 a and the image data D21 b to the subtractiondata generator 111. The subtraction data generator 111 generates thesubtraction data between these image data.

Next, referring to FIG. 2, a description is given for processing relatedto the generation and analysis of image data according to theembodiment. The subtraction data generator 111 generates image dataindicating the subtraction data between image data before contrastenhancement and image data after contrast enhancement for eachexamination (e.g., before and after a procedure). Note that in thefollowing description, the image data indicating the subtraction data issometimes referred to as “subtraction image data”. For example, asillustrated in FIG. 2, the reconstruction processor 11 obtains thesubtraction data between the image data D21 a before contrastenhancement and the image data D21 b after contrast enhancement, wherethose data were acquired before a procedure. The reconstructionprocessor 11 then generates first subtraction image data D21. Further,the reconstruction processor 11 obtains the subtraction data between theimage data D22 a before contrast enhancement and the image data D22 bafter contrast enhancement, where those data were acquired after theprocedure. The reconstruction processor 11 then generates another firstsubtraction image data D22.

Note that, the subtraction data generator 111 generates subtractionimage data based on reconstructed image data at previous example;however, this embodiment is not limited to this. For example, thesubtraction data generator 111 may obtain the subtraction data betweenthe projection data D11 a before contrast enhancement and the projectiondata D11 b after contrast enhancement, and reconstruct first subtractionimage data D21 from subtraction data of projection data. Thus, firstsubtraction image data is generated by the reconstruction processing. Inthis case, subtraction image data after a procedure is also generated inthe same manner. The subtraction image data generated by any of theabove methods corresponds to an example of “second image data”.

The subtraction data generator 111 sends any one of or a combination ofthe first subtraction image data D21 before a procedure, the image dataD21 a before contrast enhancement, and the image data D21 b aftercontrast enhancement to the analyzer 12. Similarly, the subtraction datagenerator 111 sends any one of or a combination of the first subtractionimage data D22 after the procedure, the image data D22 a before contrastenhancement, and the image data D22 b after contrast enhancement to theanalyzer 12. In the following description, it is assumed that thesubtraction data generator 111 sends the first subtraction image dataD21 before a procedure, the image data D21 a before contrastenhancement, the first subtraction image data D22 after the procedure,and the image data D22 a before contrast enhancement to the analyzer 12.

The analyzer 12 receives the first subtraction image data D21 and theimage data D21 a before contrast enhancement from the subtraction datagenerator 111. As illustrated in FIG. 2, the analyzer 12 specifies aregion, where there is no blood flow, by a technique such as theanalysis of voxel values, segmentation, or the like by using the imagedata D21 a before contrast enhancement. The no blood-flow region may be,for example, a region corresponding to the air or a region correspondingto the bone. In the following, the specified region is sometimesreferred to as “no blood-flow region”. It is not necessary to determineimage data to be analyzed in order to specify no blood-flow region. Forexample, the image data D21 b after contrast enhancement may be used.

From among voxel data in the first difference image data D21, Theanalyzer 12 sets values of voxel data corresponding to no blood-flowregion to zero on the first subtraction image data D21 to get image dataD31. Although the term “voxel data” as used herein covers the value, thevalue is sometimes described as “the value of voxel data” when the valueis emphasized in the explanation, when the value is used at the otherprocessing, or the like. In this manner, the analyzer 12 removes theregion that indicates no blood-flow region on the first subtractionimage data D21. Incidentally, an example of “third image data”corresponds to image data created by removing no blood-flow region fromthe subtraction image data.

The analyzer 12 may normalize the value of third image data based on thevalue of voxel data corresponding to the arterial region on the thirdimage data D31 to get image data D31′. In this case, the normalizedthird image data also corresponds to an example of “third image data”.

The analyzer 12 may process the voxel values of third image data to getimage data D31″ so that minute blood vessels such as a capillary isrelatively enhanced. The capillary region is a region of interest. As aspecific example, the analyzer 12 sets a region indicating the artery(major blood vessel), that is, a part which has a high value of voxeldata, to zero on third image data. Thus, the analyzer 12 removes theartery region from the third image data. In this case, the processedthird image data also corresponds to an example of “third image data”.

The analyzer 12 stores one or combinations of the third image data inthe image data storage 20. Note that the third image data may have beennormalized or processed. In addition, the analyzer 12 may store theimage data D21 a before contrast enhancement, the image data D21 b aftercontrast enhancement and information of those processing.

The analyzer 12 performs the same processing for the first subtractionimage data D21 to the first subtraction image data D22 that has beencreated based on projection data and the like after a procedure.

The registration processor 21 retrieves third image data (D31 and D32,D31′ and D32′, D31″ and D32″, or combinations) corresponding to beforeand after the procedure for comparison, and reconstruction data (D21 a,D22 a, . . . ) before contrast enhancement. It is hereinafter assumedthat the registration processor 21 has retrieved the third image dataD31 and the third image data D32, and the image data D21 a and the imagedata D22 a before contrast enhancement corresponding to the third imagedata, respectively. As described above, the third image data D31 and theimage data D21 a before contrast enhancement are image data related tothe examination that is performed before a predetermined procedure. Onthe other hand, the third image data D32 and the image data D22 a beforecontrast enhancement are image data related to the examination that isperformed after the procedure.

The registration processor 21 extracts a rigid structure, whose shapedoes not change even if the subject moves, from the image data D21 a andthe image data D22 a before contrast enhancement. The rigid structuremay be, for example, an osseous structure or the like. Such rigidstructure may be extracted, for example, based on the evaluation (e.g.,threshold processing) of voxel data. The registration processor 21allocates the image data D21 a and the image data D22 a before contrastenhancement by using the rigid structure. The registration processor 21corrects misalignment between the third image data D31 and the thirdimage data D32 based on misalignment information determined between theimage data D21 a and the image data D22 a before contrast enhancement.Thus, the third image data D31 and D32 are corrected, and then thecorrected third image data D31 c and the corrected third image data D32c are gotten. In this registration, unique anatomical coordinate systemof the third image data D31 and unique anatomical coordinate system ofthe third image data D32 are corrected based on the rigid structure.

The registration processor 21 sends the corrected third difference imagedata D31 c and the corrected third image data D32 c to the imageprocessor 22.

The image processor 22 receives, from the registration processor 21, thecorrected third image data D31 c and the corrected third image data D32c. The image processor 22 adjusts (normalizes) the voxel data of eitheror both the corrected third image data D31 c and the third image dataD32 c to get adjusted third image data D31 v and D32 v, respectively.For example, this correction is performed in the following manner: Theimage processor 22 corrects a predetermined voxel such that the voxelvalues of the third image data before and after a procedure match(indicate the same value) at the same position (the same anatomicalposition).

Incidentally, the region used at adjustment may be a region which is notaffected by a treatment or the like. The reference region for adjustmentmay be specified by, for example, an operator (doctor, etc.) through theinput unit (not illustrated).

In other words, the image processor 22 calculates an adjustment factorthat makes the values of voxel data match between specified sameanatomical region of third image data before and after a procedure. Theimage processor 22 adjusts, for example, the voxel values of all voxelsof the third image data by using the correction factor. The imageprocessor 22 may specify same region which has anatomically identicalstructure on the first subtraction image data D21 and D22, or the thirdimage data. For example, the image processor 22 may specify the sameregion based on an automatic or semi-automatic processing technique suchas segmentation, a comparison of the anatomical structure or comparisonof structural information extracted from the anatomical structure.

As described above, the image processor 22 adjusts the voxel data ofeither or both the third image data so that the voxel values match inthe same position (region) of them.

This adjustment is performed to generate second subtraction image dataD41 (described later) which is obtained by subtracting the third imagedata before and after a procedure. Incidentally, the second subtractionimage data D41 corresponds to an example of “fourth image data”.Specifically, region which is affected by a procedure or the like isrelatively enhanced by suppressing signals at specified region on thesecond subtraction image data D31. Accordingly, the above adjustment isperformed to remove the specified region as much as possible so that theregion can be negligible on the second subtraction image. Note that thesubtraction between the third image data before and after a procedurecorresponds to an example of “variation index”.

The image processor 22 may automatically determine a correction factor.The region that is affected by a treatment or the like is a minor regionin most cases. Based on this assumption, the image processor 22statistically determines a correction factor so that voxel values agreeat as many voxels as possible between the third image data before andafter a procedure. In addition, the image processor 22 obtains thesecond subtraction image data D41 (the fourth image data) by subtractingthe adjusted third image data before and after a procedure. As a result,regions which are not affected by treatment or the like disappear orbecome negligible, while regions which are affected by a treatment orthe like are relatively enhanced.

Described below are two examples of the calculation of the adjustmentfactor by the image processor 22. In first example, the image processor22 calculates an adjustment factor so that voxel data or small regionsmatch (indicate the same value) at the same position (the sameanatomical position) on the third image data before and after aprocedure. Alternatively, in the first example, the image processor 22calculates an adjustment factor so that voxel values of a plurality ofsmall regions match at the same position on the third image data beforeand after a procedure. The average value of each small region is used asthe voxel value at this time. The image processor 22 creates a histogramof adjustment factors, and determines an adjustment factor bydetermining a most frequent value from the histogram. The adjustmentfactor is applied to each region on the third image data. In secondexample, the image processor 22 determine adjustment factor so thatnegligible region which has less signals on the second subtraction imagehas maximum volume. As a result, the image processor 22 identifies anadjustment factor to maximize the volume H of voxel data or smallregions that match each other at the same position (the same anatomicalposition) of two third image data. This may be obtained by deriving a tomaximize the following equation:

$\begin{matrix}{H = {\underset{i = 1}{\overset{N}{}}\left\{ {{\alpha \; D\; 31\left( {x,y,z} \right)} - {D\; 32\left( {x,y,z} \right)}} \right\}^{2}}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack\end{matrix}$

With respect to each small region i (i=1 to N) in the operator V, theimage processor 22 checks the average value of

{αD31(x,y,z)·D32(x,y,z)}²

If the average value is within ±Δ, it is determined that they match eachother, and the count is incremented by one. On the other hand, if theaverage value is not within ±Δ, it is not counted. By this operation ofthe image processor 22, it is possible to count the number of smallregions, whose voxel values approximately match between the images, bychanging the adjustment factor α gradually. The image processor 22 maydetermine the adjustment factor by searching maximum count of matchedsmall regions.

The automatic adjustment factor (adjustment factor α) is calculatedunder the assumption that regions affected by a procedure, a treatment,or the like are minor. However, there are cases that do not conform tothis assumption. As such cases, a state in which the adjustment factor αis far off from 1 is assumed. To cope with such a situation, forexample, the image processor 22 determines whether the correction factorα falls within a range of 0.67 to 1.5. If the adjustment factor α is outof the range, the image processor 22 gives a notification (warning) tothe operator. The notification may be provided as a visual or audiowarning message. This notification may prompt the operator to specify areference region for adjustment processing. In this case, the adjustmentfactor is determined based on the specified region.

After adjusting the voxel value of each voxel on the third image databefore and after a procedure by adjustment factor α, as illustrated inFIG. 2, the image processor 22 generates the second subtraction imagedata D41 based on the subtraction between them. The second subtractionimage data D41 thus generated can show, for example, a portion (e.g.,improved or worsened part) that has been affected by a treatment or aprocedure.

The image processor 22 performs image processing on the secondsubtraction image data D41 based on predetermined image processingconditions. For example, the image processor retrieves the image dataD21 a or D22 a before contrast enhancement or the like. Then, the imageprocessor 22 may superimpose the second subtraction image data D41 onthe image data D21 a or D22 a before contrast enhancement to generateimage data D42. In the example of FIG. 2, the image data D42 shows bothanatomical structure and affected part simultaneously. With a medicalimage thus generated, for example, it is possible to identify a regionthat has changed since the examination corresponding to the firstsubtraction image data D21 until the examination corresponding to thefirst subtraction image data D22.

The image processor 22 retrieves the first subtraction image data D21acquired before a procedure or the first subtraction image data D22acquired after a procedure or the like. Further, the image processor 22may generate another image data D42 by superimposing the secondsubtraction image data D31 on one of the first subtraction image dataD21 or D22. In this manner, the image data D42 shows, for example, thepositional relationship between an affected part (e.g., infarction) onthe second subtraction image data D31 and major blood vessel (e.g.,artery). At this time, image data on which capillary is enhanced byprocessing of voxel data are used instead of the first subtraction imagedata D21 or D22 for the other purpose.

The image processor 22 retrieves, for example, the image data D21 bacquired after contrast enhancement and before a procedure or the likeinstead. Further, the image processor 22 may generate third example offusion data by superimposing the second subtraction image data D31 onthe image data D21 b after contrast enhancement. The fusion image may bedisplayed in color. If the image processor 22 generates a image data inthis manner, for example, the image data can show both the amount ofblood flow at each part before a procedure and affected portion that hasbeen affected by a procedure.

The image processor 22 sends the fusion image generated as above to thedisplay controller 23. The display controller 23 receives the fusionimage from the image processor 22, and displays it on the display 24.

Note that the image data storage 20 is only required to store image datafor generating an image to be displayed on the display 24. Therefore,the image data storage 20 does not need to store the image data D21 a,D22 a, . . . before contrast enhancement and the image data D21 b, D22b, . . . after contrast enhancement together with the first subtractionimage data D21, D22 . . . . For example, if the image data is generatedby using subtraction image data only, the image data storage 20 isrequired to store only the first subtraction image data D21, D22, . . ..

Next, referring to FIG. 3, a description is given for explainingflowchart of the image processing apparatus 100 in this embodiment. Notethat the following description is given for an example described above.

(Step S11)

The projection data storage 10 stores projection data acquired by theimaging unit 500 before and after a predetermined event, that is, theprojection data D11 a, D12 a, . . . before contrast enhancement and theprojection data D11 b, D12 b, . . . after contrast enhancement.

The reconstruction processor 11 retrieves the projection data, purposeof the acquisition in the examination, acquisition conditions such asacquisition before contrast enhancement or acquisition after contrastenhancement and acquisition time, from the projection data storage 10.The reconstruction processor 11 performs reconstruction processing to,for example, the projection data D11 a and the projection data D11 bacquired before a procedure.

By the reconstruction processing, the image data D21 a before contrastenhancement and the image data D21 b after contrast enhancement aregenerated. When retrieving the projection data D12 a and D12 b after theprocedure, the reconstruction processor 11 performs the reconstructionprocessing to them to generate the image data D22 a before contrastenhancement and the image data D22 b after contrast enhancement.

The reconstruction processor 11 stores, in the image data storage 20,the image data D21 a in association with the image data D21 b aftercontrast enhancement, and the image data D22 a in association with theimage data D22 b.

(Step S12)

Besides, the subtraction data generator 111 receives the image databefore contrast enhancement and the image data after contrastenhancement from the reconstruction processor 11 in step S11, andobtains the subtraction data between them.

By obtaining the subtraction, the subtraction data generator 111generates, for example, the first subtraction image data D21 thatindicates the subtraction between the image data D21 a before contrastenhancement and the image data D21 b after contrast enhancement (seeFIG. 2). The subtraction data generator 111 further generates the firstsubtraction image data D22 that indicates the subtraction between theimage data D22 a before contrast enhancement and the image data D22 bafter contrast enhancement.

The subtraction data generator 111 may obtain the subtraction betweenthe projection data before contrast enhancement and that after contrastenhancement, and generate the subtraction image data by reconstructingthe subtraction of the projection data.

(Step S13)

The subtraction data generator 111 sends at least one of the firstsubtraction image data D21 generated, the image data D21 a beforecontrast enhancement, and the image data D21 b after contrastenhancement to the analyzer 12. For example, the subtraction datagenerator 111 sends the first subtraction image data D21 and the imagedata D21 a before contrast enhancement to the analyzer 12. Similarly,the subtraction data generator 111 sends the first subtraction imagedata D22 and the image data D22 a before contrast enhancement to theanalyzer 12.

The analyzer 12 receives the first subtraction image data D21 and theimage data D21 a before contrast enhancement from the subtraction datagenerator 111. The analyzer 12 specifies no blood-flow region in theimage data D21 a before contrast enhancement (see FIG. 2). The imagedata D21 b after contrast enhancement may be used instead to specify noblood-flow region.

The analyzer 12 sets the value of voxel data corresponding to the noblood-flow region to zero. In this manner, the analyzer 12 removes theno blood-flow region from the first subtraction image data D21 to getimage data D31.

The analyzer 12 may normalize the value of the subtraction image dataD31 based on the values of voxel data corresponding to the arterialregion.

In addition, the analyzer 12 may set the voxel value of, for example, apart indicating an artery with a high voxel value to zero in the imagedata D31 to relatively enhance minute blood vessels such as capillaries.

The analyzer 12 stores the image data D31 or the like in the image datastorage 20. The analyzer 12 performs the same processing on the firstdifference image data D22 to get image data D32 or the like.

(Step S14)

The registration processor 21 retrieves the image data D31 (before aprocedure) and the image data D32 (after the procedure), and also theimage data D21 a and the image data D22 a before contrast enhancementcorresponding to them, respectively.

The registration processor 21 extracts a rigid anatomical structure(bone, etc.), whose shape does not change even if the subject moves,from the image data D21 a and the image data D22 a before contrastenhancement by threshold processing or the like. The registrationprocessor 21 allocates the image data D21 a and the image data D22 abefore contrast enhancement by using extracted information. Theregistration processor 21 corrects the position and orientation of theimage data D31 and the image data D32 based on allocation informationbetween the image data D21 a and D22 a before contrast enhancement.Thus, the image data D31 and D32 are corrected.

The registration processor 21 sends the corrected image data D31 c andthe corrected image data D32 c, to the image processor 22.

(Step S15)

The image processor 22 receives, from the registration processor 21, thecorrected image data D31 c and the corrected image data D32 c. The imageprocessor 22 adjusts either or both the corrected image data D31 and thecorrected image data D32 so that voxel values match at the same regionbetween the two image data D21 and D22. The same region which is notaffected by a treatment is determined by the operator via the input unit(not illustrated), for example. The image processor 22 calculates anadjustment factor that makes the values of voxel data match betweenregions each specified on the corrected image data. The image processor22 adjusts each voxel value of the corrected image data based on theadjustment factor.

First example or second example described above may be employed toautomatically determine the adjustment factor.

(Step S16)

After adjusting each voxel value on the corrected image data D31 c andthe corrected image data D32 c, the image processor 22 obtains thesubtraction between these image data and thereby generates the secondsubtraction image data D31 (see FIG. 2).

(Step S17)

The image processor 22 performs image processing on the secondsubtraction image data D41 based on predetermined image processingconditions. For example, the image processor retrieves the image dataD21 a or D22 a before contrast enhancement and before or after aprocedure or the like used to generate the second subtraction image dataD41. Then, the image processor 22 may superimpose the second subtractionimage data D41 on the image data D21 a or D22 a before contrastenhancement to generate fusion image data D42. For example, in theexample of FIG. 2, the fusion image data D32 is a schematic illustrationof image data obtained by superimposing the second subtraction imagedata D41 on the image data D21 a before contrast enhancement. The fusionimage thus generated shows, for example, both improved or worsenedregion by a procedure and anatomical information simultaneously.

The image processor 22 may generate a fusion image by superimposing thesecond subtraction image data D41 on the first subtraction image dataD21 or D22. At this time, fusion image shows relationship betweenaffected region and major blood vessels.

The image processor 22 may generate a fusion image displayed in color bysuperimposing the second subtraction image data D41 on the image dataD21 b after contrast enhancement.

The image processor 22 sends the fusion image generated in the abovemanner to the display controller 23. The display controller 23 receivesthe fusion image from the image processor 22, and displays it on thedisplay 24.

As described above, the image processing apparatus 100 of thisembodiment generates subtraction image data indicating affected regionby a procedure. With this configuration of the image processingapparatus 100 of the embodiment, an affected part, which has newlydeveloped from before to after a predetermined event such as aprocedure, can be displayed with additional information by fusing theanatomical information such as osseous or vessel structures.

(Modification)

The image processing apparatus 100 of the embodiment is described aboveas obtaining the subtraction between the first subtraction image databetween image data before and after a procedure or the like (D31 andD32, an example of “third image data”), and thus generating the secondsubtraction image data D41 (an example of “fourth image data”). However,the embodiment is not limited to this and may be constructed as follows,for example.

After correcting the voxel value of each voxel in the corrected imagedata D31 c acquired prior to a procedure and the first corrected imagedata D32 c acquired after the procedure, the image processor 22 dividesthe image data D32 by the image data D31. A specific example isrepresented by the following equation, where PB (x, y, z) is thecorrected image data D31 c before a procedure, and PA (x, y, z) is thecorrected image data D32 c after the procedure.

$\begin{matrix}\frac{{PA}\left( {x,y,z} \right)}{{PB}\left( {x,y,z} \right)} & \left\lbrack {{Equation}\mspace{14mu} 2} \right\rbrack\end{matrix}$

The image processor 22 generates the second subtraction image data D41by the division of these image data.

Incidentally, there are cases where the value of PB (x, y, z) in theabove equation is approximately zero. In such a case, the subtractionvalue may be close to infinite number. Accordingly, when the value of PB(x, y, z) is. close to zero, the image processor 22 adds a predeterminedconstant to PB (x, y, z), and then divides the corrected image data D32c by the corrected image data D31 c. A specific example is representedby the following equation, where “k” indicates the constant.

$\begin{matrix}\frac{{PA}\left( {x,y,z} \right)}{{{PB}\left( {x,y,z} \right)} + k} & \left\lbrack {{Equation}\mspace{14mu} 3} \right\rbrack\end{matrix}$

Incidentally, the result of dividing the corrected image data D32 c bythe corrected image data D31 c corresponds to an example of “variationindex”.

In this modification, as in the embodiment described above, the imageprocessing apparatus 100 generates subtraction image data indicatingaffected region by a procedure. Further, the image processing apparatus100 divides the corrected image data after the predetermined event bythe corrected image data before the predetermined event. The imageprocessing apparatus 100 of the modification displays a fusion imagegenerated based on this division or subtraction on the display 24. Withthis configuration of the image processing apparatus 100 also, anaffected part, which has newly developed from before to after apredetermined event such as a procedure, can be displayed in a mannerrecognizable by the viewer of the image.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. An image processing apparatus, comprising adisplay; and processing circuitry configured to generate second imagedata by subtracting first image data after contrast enhancement fromfirst image data before contrast enhancement, remove informationindicating a structure from a plurality of the second image data togenerate third image data, correct misalignment of a plurality of thethird image data based on characteristics of shape of the subjectrepresented in at least any one of the first image data, the secondimage data, and the third image data, obtain a variation index betweenthe third image data after alignment correction, and generate fourthimage data base on the variation index, and display a medical imagebased on the fourth image data on the display.
 2. The image processingapparatus of claim 1, wherein the processing circuitry is furtherconfigured to generate the second image data based on the first imagedata before contrast enhancement and the first image data after contrastenhancement acquired before a predetermined event, generate the secondimage data based on the first image data before contrast enhancement andthe first image data after contrast enhancement acquired after thepredetermined event, and correct misalignment of the third image databefore the predetermined event and the third image data after thepredetermined event.
 3. The image processing apparatus of claim 2,wherein the predetermined event includes procedure, therapy, ortreatment for the subject.
 4. The image processing apparatus of claim 2,wherein the processing circuitry is further configured to obtain adifference between the third image data as the variation index aftermisalignment correction, and generate the fourth image data base on thedifference.
 5. The image processing apparatus of claim 4, wherein theprocessing circuitry is further configured to divide the third imagedata before the predetermined event by the third image data after thepredetermined event, and generate the fourth image data base on a resultof division as the variation index.
 6. The image processing apparatus ofclaim 5, wherein the processing circuitry is further configured to add apredetermined constant to the third image data before the predeterminedevent or the third image data after the predetermined event prior to thedivision.
 7. The image processing apparatus of claim 1, wherein theprocessing circuitry is further configured to specify thecharacteristics of the shape in at least either one of the first imagedata before contrast enhancement and the first image data after contrastenhancement.
 8. The image processing apparatus of claim 7, wherein theprocessing circuitry is further configured to specify thecharacteristics of the shape from a region corresponding to a bone or aregion corresponding to air in the first image data before contrastenhancement or the first image data after contrast enhancement.
 9. Theimage processing apparatus of claim 1, wherein the processing circuitryis further configured to adjust voxel values of the third image datasuch that voxel values match between anatomically identical regions ofthe subject in the third image data, and obtain the variation indexbetween the third image data after adjustment.
 10. The image processingapparatus of claim 1, wherein the processing circuitry is furtherconfigured to adjust voxel values of the third image data such thatvoxel values match between the third image data based on a regionspecified through an input circuit, and obtain the variation indexbetween of the third image data after adjustment.
 11. The imageprocessing apparatus of claim 9, wherein the voxel values are averagevalues of the voxel values in the anatomically identical regions of thesubject, and the processing circuitry is further configured to calculatean adjustment factor based on the third image data before obtaining thevariation index to achieve a largest regions in which the average valuesmatch between the third image data.
 12. The image processing apparatusof claim 11, wherein the processing circuitry is further configured to,when the adjustment factor falls out of a predetermined range, stopcalculation of the adjustment factor and output a warning.
 13. The imageprocessing apparatus of claim 2, wherein the processing circuitry isfurther configured to display the medical image on the display based onthe first image data before contrast enhancement and the fourth imagedata.
 14. The image processing apparatus of claim 2, wherein theprocessing circuitry is further configured to display the medical imageon the display based on the second image data and the fourth image data.15. The image processing apparatus of claim 2, wherein the processingcircuitry is further configured to display the medical image on thedisplay based on the first image data after contrast enhancement and thefourth image data.
 16. The image processing apparatus of claim 15,wherein the processing circuitry is further configured to display themedical image obtained by superimposing the fourth image data on thefirst image data on the display.
 17. The image processing apparatus ofclaim 14, wherein the processing circuitry is further configured todisplay the medical image obtained by superimposing the fourth imagedata on the second image data on the display.
 18. An image processingmethod, comprising: generating second image data indicating a differencebetween first image data before contrast enhancement and first imagedata after contrast enhancement, the first image data being acquired bycapturing an image of a subject at different times, removing informationindicating a structure from a plurality of the second image datacorresponding to the different times to generate third image data,correcting misalignment of a plurality of the third image data based oncharacteristics of shape of the subject represented in at least any oneof the first image data, the second image data, and the third imagedata, obtaining a variation index between the third image data aftermisalignment correction, and generating fourth image data base on thevariation index, and displaying a medical image based on the fourthimage data on a display.
 19. The image processing method of claim 18,wherein the generating includes generating the second image data basedon the first image data before contrast enhancement and the first imagedata after contrast enhancement acquired before a predetermined event,and generating the second image data based on the first image databefore contrast enhancement and the first image data after contrastenhancement acquired after the predetermined event, and the correctingincludes correcting the misalignment of the third image data before thepredetermined event and the third image data after the predeterminedevent.